De-occlusion. De-occlusion aims at recovering the appearance content of the invisible occluded parts. SeGAN [1]} adopts a residual network based model for mask completion and inferring the appearance of the invisible parts of indoor objects. Yan et al.  [2]} presented an iterative multi-task framework for amodal mask completion and de-occlusion of vehicles. Zhou et al.  [3]} built upon a well-known inpainting approach [4]} and proposed to reason about the occluded regions and recover the appearance content of humans. Baek et al.  [5]} presents a weakly-supervised method to adapt from hand-object domain to single hand-only domain. However, its image generation module and the pose estimator are deeply coupled together, limiting its generalization ability to adapt to different hand pose estimators and resulting in low-quality restored image.
